Hotspot Zones Research Articles

Improved the Sensitivity and Limit of Detection of Surface Alloying SERS Sensors by Controlling Mixing Ratio of Trimetallic (Ag-Au-Pd) Nanoparticles

In this study, three different types of hybrid structures SERS sensors made from different mixing ratio of trimetallic (Ag-Au-Pd) nanoparticles of [Au1: Ag1: Pd1] , [Au1: Ag2: Pd1] , [Au1: Ag2: Pd2] in surface alloying forms deposited on macro porous silicon (macroPsi) layer were created and extensively tested. By using a simple and fast immersion process, trimetallic (Ag-Au-Pd) nanoparticles were created utilizing ion reduction of numerous metallic salts on a macro porous silicon (macroPsi) layer. The macroPsi layers were created using a laser assisted etching (LAE) technique for 15 minutes with a constant density of approximately (28mA/c.m2). At a constant concentration (10-3 M) of HAu.Cl4, Ag.NO3 and Pd.Cl2 to synthesize Au-NPs, Ag-NPs and Pd-NPs, immersion processes with various mixing ratios were performed. The hybrid structures of SERS sensors were examined using XRD, FE-SEM, and Raman microscopes. The sensors were tested at different concentrations from 10-6 to 10-12 of MB target molecules. The SERS trimetallic sensors demonstrated a considerable reliance on hot spot zones among trimetallic nanoparticles. Higher enhancement factor with lower detection limit of Raman signal was obtained from [Au1: Ag2: Pd2] hybrid structures SERS sensor of about 1.5×1010 and 10-14 respectively, due to extra ordinary specific surface area and high surface density forming trimetallic nanoparticles . The variations of mixing ratio of trimetallic (Ag-Au-Pd) provide an effective pathway to develop the sensors performance towards detection of lower concentrations.

Continuum models for meso-scale simulations of HMX (1,3,5,7-tetranitro-1,3,5,7-tetrazocane) guided by molecular dynamics: Pore collapse, shear bands, and hotspot temperature

Meso-scale calculations of energy localization and initiation in energetic material microstructures must capture the deformation and collapse of pores and high-temperature shear bands, which lead to hotspots. Because chemical reaction rates depend sensitively on temperature, predictive continuum models need to get the pore-collapse dynamics and resulting hotspot temperatures right; this imposes stringent demands on the fidelity of thermophysical model forms and parameters and on the numerical methods employed to perform high-resolution meso-scale calculations. Here, continuum material models for β-HMX are examined in the context of shock-induced pore collapse, treating predictions from all-atom molecular dynamics (MD) simulations as ground truth. Using atomistics-consistent material properties, we show that the currently available strength models for HMX fail to correctly capture pore collapse and hotspot temperatures. Insights from MD are then employed to advance a Modified Johnson–Cook (M-JC) strength model, which is shown to capture key aspects of the physics of shock-induced localization in HMX. The study culminates in a MD-guided strength model for β-HMX that produces continuum pore-collapse results in better alignment on several aspects with those predicted by MD, including pore-collapse mechanism and rate, shear-band formation in the collapse zone, and temperature, strain, and stress fields in the hotspot zone and the surrounding material. The resulting MD-informed/MD-determined M-JC model should improve the fidelity of meso-scale simulations to predict the detonation initiation of HMX-based energetic materials in microstructure-aware multi-scale frameworks.

The effect of redox fluctuation on carbon mineralization in riparian soil: An analysis of the hotspot zone of reactive oxygen species production

Riparian zones are important depositional environments at the catchment scale and provide environmental services such as carbon sequestration. This zone is a highly dynamic interface for oxygen and electron exchange, which confers the basis for reactive oxygen species (ROS) production. However, the differences in soil ROS production and their impact on carbon turnover across various redox locations within the riparian zone remain to be fully elucidated. In this study, we investigated the distribution characteristics and generation mechanism of ROS in riparian soil based on soil samples collected in a three-month field monitoring experiment, with additional incubation experiments conducted to examine the effect of hydroxyl radical (•OH) on soil organic carbon (SOC) mineralization. The obtained results demonstrated that the riverine wetland was the hotspot zone for •OH production, with the production flux of 13.05 μmol kg−1 d−1, which was significantly higher than that in floodplain (7.29 μmol kg−1 d−1) and riverbank soils (8.61 μmol kg−1 d−1). Moreover, •OH levels displayed distinct rhythmic fluctuations, with significantly higher concentrations at low water levels compared to those at high water levels, and remained essentially flat over three cycles. The statistic analysis revealed that the ROS production was highly dependent on reduced species and microbial community structure, which function as biogeochemical batteries and electron shuttles under redox fluctuations. Furthermore, the generated •OH involved in the abiotic mineralization of SOC, contributing to 13.1‒21.8 % of total CO2 efflux. Compared to particulate organic carbon (POC), mineral-associated organic carbon (MAOC) fractions of SOC were more susceptible to •OH attacks. The findings provide a novel insight to comprehensively assess the redox process on riparian carbon turnover.

Exploring dispersion modelling for resuspended pollen particles in a heterogeneous urban environment

ABSTRACT Urban air quality assessment in regard to biogenic emissions such as airborne pollen grains (PGs) is indispensable in the urban environment which significantly affects human health, climates, ecosystems, and energy production. Dispersion and deposition of PGs because of resuspension events (ReSE) are investigated using computational fluid dynamics (CFD). The mechanisms in PGs movement are achieved through the Lagrangian formulation-based discrete-phase model (DPM). The observed pollen grains distribution pattern is highly spatially dependent on heterogeneous urban obstacles as it induces modified characteristics of the wind flow field. Wind-urban structure interplays in PGs concentration variation. The simulated results identify the pollen hot-spot zones, where vortical flow features and low wind speed dominate. This approach offers a detailed understanding of the dispersion patterns of airborne PGs in urban environments because of ReSE with high resolution.

Thermo-electric modeling and analysis of lithium-ion battery pack for E-mobility

AbstractElectric Vehicles (EVs) have emerged as a viable and environmentally sustainable alternative to traditional internal combustion vehicles by utilizing a clean energy source. The advancement and expansion of electric cars rely on the progress of electrochemical batteries. The utilization of Lithium-Ion Batteries is widespread primarily because of its notable energy density. Changes influence the performance of these batteries in temperature. The Thermal Management System of the battery is one of the very important systems in EVs to improve the performance and life of the battery. The geometrical spacing of the cell modules is considered identical for a more accurate comparison of temperature distribution. For better cooling and heat dissipation, the battery pack’s two sides are kept entirely open to facilitate the inflow of air. In this work, active BTMS solutions are selected and analyzed using the development of three-dimensional free, open-source OpenFOAM computational fluid dynamics simulations for accurate thermal modeling and hotspot zones in cylindrical battery packs. The outcome of the simulations is compared using parameters like temperature distribution in battery cells, battery modules, and heat generation. Among all the cell temperature zones, the temperature maximum is near the sixth cell of the module depth. OpenFOAM results validated with the existing literature’s experimental and Ansys results. Air cooling is utilized for cooling performance because of its relatively simple structure and lightweight.

Bidirectional interlayer cooling of 3D chip stack for temperature uniformity enhancement and hotspot mitigation

In this work, bidirectional interlayer cooling with both enhanced temperature uniformity and low flow resistance is proposed for effective 3D chip stack thermal management. Staggered uniform micropin fins and curved entrance and exit regions are elaborately designed. Experimental and numerical studies are carried out to investigate the cooling characteristics under different Reynolds numbers and heat fluxes. In comparison with common interlayer cooling solutions, counter-flow arrangement is achieved between the layers and vertical heat transfer within the chip stack is utilized. Heat fluxes of 300 W/cm2 and 100 W/cm2 are dissipated in the hotspot and background zones respectively. Total thermal resistance of 0.18 K/W is obtained. The maximum decrease in temperature difference is up to 66.2%. Both thermal resistance and coefficient of performance (COP) are considered and compared with the results from previous studies for comprehensive performance evaluation. The maximum COP of 11,523 is reached. The thermal and hydraulic performance advantages of the present design can be verified. The bidirectional interlayer cooling offers new potential for thermal management of 3D chip stack.

Spatial-temporal change of river thermal environment and anthropogenic impact in China

River water temperature is important and closely related to river ecosystem, concerning fishery industry, human health, and the land-sea exchange of nutrients, especially for great powers with a good deal of heat emission from once-through cooling systems of thermal power plants. However, the changes in river water temperature under the joint action of climate change and human activity such as the heat emission have not been well investigated for rising powers, hampering environmental policy making for sustainable development. Therefore, we have taken advantage of a recently-developed land surface model including river water temperature calculation with anthropogenic thermal discharge and zonal statistics to quantitatively make out the river water temperature variation and the man-made influence over the past thirty years (1981–2010) in China for the first time. Results show that the estimated water temperature in major rivers is generally close to the observation with the r2 of 0.83, though the underestimation exists in some rivers. The river water in the Pearl River Basin was the warmest with the mean temperature of 19.2 °C and the others in order were in the Southeast Basin, the Huaihe River Basin, the Yangtze River Basin, the Haihe River Basin, the Yellow River Basin, the Southwest Basin, the Song-Liao River Basin, and the Continental Basin, ranging from 16.7 °C to 6.3 °C. The Huaihe River Basin had the fastest mean increase rate of ca. 0.27 °C decade−1, while the slowest mean increase rate of ca. 0.13 °C decade−1 existed in the Pearl River Basin. At the subbasin scale, a meridionally-distributed hot spot zone (along the 115°E) of the increasing water temperature has been identified, where the trends ranged from 0.2 °C decade−1 to 0.5 °C decade−1. Air temperature exerted a major control on the spatial pattern of climatological water temperature, while both air temperature and downwelling solar flux played a leading role in the distribution of water temperature change trends. Although anthropogenic thermal emission heated the rivers locally, the impacts in the Song-Liao River, the Haihe River, the Huaihe River, and the middle and lower reaches of Yellow River and Yangtze River had been raised up to ca. 4.5 °C, when comparing with those controlled by climate change only. In general, these results show an acceptable level of river water temperature simulation in the land surface model, and could provide a scientific reference for the assessment on riverine thermal environment under the climate change and social impact in China.

Mitigated N2O emissions from submerged-plant-covered aquatic ecosystems on the Changjiang River Delta

Submerged plants affect nitrogen cycling in aquatic ecosystems. However, whether and how submerged plants change nitrous oxide (N2O) production mechanism and emissions flux remains controversial. Current research primarily focuses on the feedback from N2O release to variation of substrate level and microbial communities. It is deficient in connecting the relative contribution of individual N2O production processes (i.e., the N2O partition). Here, we attempted to offer a comprehensive understanding of the N2O mitigation mechanism in aquatic ecosystems on the Changjiang River Delta according to stable isotopic techniques, metagenome-assembly genome analysis, and statistical analysis. We found that the submerged plant reduced 45 % of N2O emissions by slowing down the dissolved inorganic nitrogen conversion velocity to N2O in sediment (Vf-[DIN]sed). It was attributed to changing the N2O partition and suppressing the potential capacity of net N2O production (i.e., nor/nosZ). The dominated production processes showed a shift with increasing excess N2O. Meanwhile, distinct shift thresholds of planted and unplanted habitats reflected different mechanisms of stimulated N2O production. The hotspot zone of N2O production corresponded to high nor/nosZ and unsaturated oxygen (O2) in unplanted habitat. In contrast, planted habitat hotspot has lower nor/nosZ and supersaturated O2. O2 from photosynthesis critically impacted the activities of N2O producers and consumers. In summary, the presence of submerged plants is beneficial to mitigate N2O emissions from aquatic ecosystems.

Geo-visualizing the hotspots of smog-induced health effects in district Gujranwala, Pakistan: a community perspective.

This study aims to examine the health effects of smog on different age groups in Gujranwala and its associated health effects. To achieve this, primary data was gathered through a questionnaire survey focused on health issues faced by elderly individuals during the smog season. The results of the survey revealed that older adults in Gujranwala are particularly vulnerable to a range of health problems during this period, including coughing, throat infections, irritated eyes, runny noses, shortness of breath, chest pain while breathing, wheezing, asthma, heart problems, and respiratory issues. In order to analyze the spatial distribution of these health concerns, spatial and geo-statistical methods were employed utilizing ArcGIS 10.5. By integrating field data and secondary sources, hotspot and cold spot zones were identified. Employing the statistical model within ArcMap 10.5, hotspot analysis was performed to determine areas with elevated air quality index (AQI) values and associated health problems. The application of the inverse distance weighted approach, incorporating the Z value, facilitated a visual representation of areas with heightened and reduced AQI and health-related issues. The study's outcomes underscore the prevalence of health challenges among older adults during the winter months in Gujranwala, particularly linked to smog-induced throat infections, irritated eyes, and runny noses. The research identified zones with escalated AQI values, encompassing regions such as Gujranwala, Chandaqella, Alam Chowk, Khali Shahpur, Sialkot Bypass, and Pindi Bypass. It was established that industrial pollutants and vehicular emissions are the primary contributors to smog in the area. Given the detrimental consequences of pollution on individuals of all age groups, it is imperative to take action to mitigate its impact. This can be achieved through addressing pollution sources, implementing effective emission control measures, and fostering public awareness. By adopting proactive measures, the adverse health effects of pollution can be minimized, thereby fostering a healthier and safer environment for the entire population. This study offers valuable insights for policymakers and environmentalists to implement targeted interventions and improve air quality, ultimately safeguarding the health of local populations.

Spatial and Temporal Analysis: A GIS-Based Application Tool for Crime Monitoring and Clustering in Malawi

For the purposes of monitoring, evaluating, and conducting a geographical analysis of crime-related data, the study used geospatial technology to collect crime data based on spatial location and the Malawi Police Data Digest of 2019 and 2020. In a more generic sense, knowing the geographic patterns of crime in Malawi using GIS technology can help determine how to make and implement important decisions to reduce crimes in Malawi. The Malawi Police Service has established a number of database management systems to help with crime monitoring. Notwithstanding, it has not yet fully integrated Geographic Information Systems across all jurisdictions. Maps showing crime locations and crime hotspot zones are therefore not included in the crime data and statistics report provided by the Malawi Police Service. In this light, a lot of people have become victims of various forms of crimes in areas where those crimes are also prevalent. To collect, track, and analyze crime data in Malawi for this study, Geographical Information System (GIS) particularly network analysis techniques were used. Network Analysis was used to identify crime hotspots by analyzing crime data as a network of interconnected events and locations. The rationale behind this was to treat each crime event as a node in the network and the spatial relationships between the crimes as edges. By analyzing this network, patterns and relationships between crime events were revealed, allowing for the identification of crime hotspots. The study found that Lilongwe in the central region and the capital city registered the highest number of crimes seconded by Blantyre in the southern region and followed by Mangochi. Mzimba recorded high crimes in the northern region. In Malawi, the traditional systems of intelligence and criminal record keeping have failed to satisfy the demands of today's crime situation. Manual methods neither give accurate, dependable, or complete data 24 hours a day nor do they help in trend forecasting and decision assistance. It also leads to poorer productivity and inefficient workforce use. The appropriate application of information technology is the solution to this ever-increasing challenge.

Exploring urban land surface temperature using spatial modelling techniques: a case study of Addis Ababa city, Ethiopia

Urban areas worldwide are experiencing escalating temperatures due to the combined effects of climate change and urbanization, leading to a phenomenon known as urban overheating. Understanding the spatial distribution of land surface temperature (LST) and its driving factors is crucial for mitigation and adaptation of urban overheating. So far, there has been an absence of investigations into spatiotemporal patterns and explanatory factors of LST in the city of Addis Ababa. The study aims to determine the spatial patterns of land surface temperature, analyze how the relationships between LST and its factors vary across space, and compare the effectiveness of using ordinary least squares and geographically weighted regression to model these connections. The findings showed that the spatial patterns of LST show statistically significant hot spot zones in the north-central parts of the study area (Moran’s I = 0.172). The relationship between LST and its explanatory variables were modelled using ordinary least square model and thereby tested if there is spatial dependence in the model using the Koenker (BP) Statistic.The result revealed non-stationarity (p = 0.000) and consequently geographically weighted regression was employed to compare the performance with OLS. The research has revealed that, GWR (R2 = 0.57, AIC = 1052.1) is more effective technique than OLS (R2 = 0.42, AIC = 2162.0) for studying the relationship LST and the selected explanatory variables. The use of GWR has improved the accuracy of the model by capturing the spatial heterogeneity in the relationship between land surface temperature and its explanatory variables. The relationship between LST and its explanatory variables were modelled using ordinary least square model and thereby tested if there is spatial dependence in the model using the Koenker (BP) Statistic. The result revealed non-stationarity ((p = 0.000) and consequently geographically weighted regression was employed to compare the performance with OLS. The research has revealed that, GWR (R2 = 0.57, AIC = 1052.1) is more effective technique than OLS (R2 = 0.42, AIC = 2162.0) for studying the relationship LST and the selected explanatory variables. The use of GWR has improved the accuracy of the model by capturing the spatial heterogeneity in the relationship between land surface temperature and its explanatory variables. Consequently, Localized understanding of the spatial patterns and the driving factors of LST has been formulated.

Mapping the spatial disparities of HIV prevalence in Ethiopian zones using the generalized additive model

HIV is a worldwide social and health pandemic that poses a significant problem. This study contributes to the 2030 global agenda of reducing HIV prevalence. The study analyzed HIV prevalence using the 2016 Ethiopian Demographic and Health Survey data. The study included men aged 15–54 years and women aged 15–49 years who responded to questions about HIV tests. A generalized geo-additive model (GAM) was fitted to HIV data using nonparametric smooth terms for geolocations. Two smoothing techniques were used in GAMs to evaluate spatial disparities and the probable effects of variables on HIV risk. There were certain areas in Ethiopia that were identified as hot spot zones for HIV, including Nuer and Agnuak in Gambella, West Wollega and Illubabor in Oromia, Benchi Maji and Shaka in SNNPR, Awsi, Fantana, Kilbet, and Gabi in the Afar region, Shinilie of the Somalia region, North and South Wollo, Oromia special zones of the Amhara region, Central Ethiopia, and Addis Ababa city. On the other hand, the eastern parts of Ethiopia, particularly most zones in the Somalia region, were identified as cold spot zones with the lowest HIV odds ratio. The odds of HIV+ were higher for those who reside in rural areas than in urban areas. Furthermore, people who have STIs, who used contraceptive methods, and who learned at the secondary level of education were more likely to be infected with HIV. After adjusting for confounding variables, the results indicated that there are substantially significant spatial variations in HIV prevalence across Ethiopian zones. These results provide essential information to strategically target geographic areas to allocate resources and policy interventions at zonal level administrations.

The spatial effects of the household's food insecurity levels in Ethiopia: by ordinal geo-additive model.

Food insecurity and vulnerability in Ethiopia are historical problems due to natural- and human-made disasters, which affect a wide range of areas at a higher magnitude with adverse effects on the overall health of households. In Ethiopia, the problem is wider with higher magnitude. Moreover, this geographical distribution of this challenge remains unexplored regarding the effects of cultures and shocks, despite previous case studies suggesting the effects of shocks and other factors. Hence, this study aims to assess the geographic distribution of corrected-food insecurity levels (FCSL) across zones and explore the comprehensive effects of diverse factors on each level of a household's food insecurity. This study analyzes three-term household-based panel data for years 2012, 2014, and 2016 with a total sample size of 11505 covering the all regional states of the country. An extended additive model, with empirical Bayes estimation by modeling both structured spatial effects using Markov random field or tensor product and unstructured effects using Gaussian, was adopted to assess the spatial distribution of FCSL across zones and to further explore the comprehensive effect of geographic, environmental, and socioeconomic factors on the locally adjusted measure. Despite a chronological decline, a substantial portion of Ethiopian households remains food insecure (25%) and vulnerable (27.08%). The Markov random field (MRF) model is the best fit based on GVC, revealing that 90.04% of the total variation is explained by the spatial effects. Most of the northern and south-western areas and south-east and north-west areas are hot spot zones of food insecurity and vulnerability in the country. Moreover, factors such as education, urbanization, having a job, fertilizer usage in cropping, sanitation, and farming livestock and crops have a significant influence on reducing a household's probability of being at higher food insecurity levels (insecurity and vulnerability), whereas shocks occurrence and small land size ownership have worsened it. Chronically food insecure zones showed a strong cluster in the northern and south-western areas of the country, even though higher levels of household food insecurity in Ethiopia have shown a declining trend over the years. Therefore, in these areas, interventions addressing spatial structure factors, particularly urbanization, education, early marriage control, and job creation, along with controlling conflict and drought effect by food aid and selected coping strategies, and performing integrated farming by conserving land and the environment of zones can help to reduce a household's probability of being at higher food insecurity levels.

Numerical Simulation of Heat Transfer Behavior in Hot Spot Zone of Converter Molten Bath

The multiphase fluid flow and heat transfer behavior in the steelmaking converter are essential for efficient and stable smelting. Herein, a mathematical model of heat transfer for the hot spot zone of the converter is established. The temperature distribution of the molten bath is clarified, and the effects of operating parameters and the exothermic rate of hot spot zone on the heat transfer behavior of the molten bath are investigated. The results show that the temperature gradient between the hot spot zone and the liquid steel as well as the circulation of liquid steel will lead to the nonuniform distribution of temperature. Both the direction of flow field and the mixing characteristics of molten bath influence the heating rate of molten bath, whereas the direction of the flow field plays a dominant role. In addition, the oxygen lance height has a significant influence on the heat transfer behavior; as the lance height increases from 1.35 to 1.65 m, the heating rate increases by 68.32%. The increase of exothermic rate will remarkably increase the temperature gradient and the heating rate increases by 65.84% and 114.28% when the exothermic rate increases by 1.5 and 2 times, respectively.

Impacts of the Middle Route of China's South-to-North Water Diversion Project on the water network structure in the receiving basin.

The Middle Route of South-to-North Water Diversion Project (MRSNWD) is the main skeleton of China's National Water Network, its construction has changed the structure of the original water network, and analyzing the topological change of the water network in context with MRSNWD is significant for water network planning and management. In this study, the overall network characteristics of the water network in 2010 and 2020 were analyzed based on the small-world and scale-free characteristics of complex network theory. The topological changes of the water network from a node perspective were examined using three network centrality indexes: degree centrality (DC), closeness centrality (CC), and betweenness centrality (BC), while assessing the important nodes of the water network and recognizing functional areas of cold-hot spots. The results show that the water network's centrality in the study area improved after the project construction, with the average degree of the water network increasing from 2.39 to 2.42 and the average path length decreasing from 111.81 to 97.08. The propagation efficiency and network stability also increased, with a rise in important node proportion from 9.8 to 14.4%. The nodes in the DC hotspot zone along the project route have increased by 1.5%, implying an increase in the connectivity of the water network, while MRSNWD optimizes its north-south hub propagation path. "Small-worldness" indicates that most nodes of a network can be accessed and connected over shorter paths. The water network has a significant "small-worldness" and has been enhanced by the MRSNWD's construction. Approximating the water network as a scale-free network can impact its security by identifying critical nodes. The results of this research can provide the necessary technical support and reference significance for China's National Water Network.

Landslide Susceptibility and Risk Mapping in the Tectonic Ensemble Comprising of Eastern Himalayan Zone, Northeast India and Bhutan using Logistic Regression and Random Forest Techniques

Abstract The mountainous terrain of the East-Northeast tectonic ensemble covers an approximate area of 3,11,420 km2 comprising the Eastern Himalayan zone, Northeast India and Bhutan is prone to mass movements. The increasing trend of landslide occurrences in the last few decades sets loud alarm bells for mapping the landslide hotspot zones in terms of Landslide Susceptibility and Risk. Initially, a landslide inventory of around 9751 landslides has been prepared, of which 6826 (70%) has been randomly picked as training dataset and the remaining 2925 (30%) as the testing dataset. Thereafter, Random Forest (RF) and binary Logistic Regression (LR) based Landslide Susceptibility Zonation (LSZ) have been prepared through twelve predictor layers viz. Slope Angle, Slope Aspect, Slope Curvature, Distance to Drainage, Distance to Lineament, Landform, Surface Geology, Distance to Road, Normalized Difference Vegetation Index (NDVI), Landuse/ Landcover (LULC), Rainfall and Epicentre Proximity. Both the Landslide Susceptibility Index (LSI) maps are divided into five classes, viz. ‘low’, ‘moderate’, ‘high’, ‘very high’ and ’severe’, which are then validated statistically by drawing a comparison with the 30% testing chronological landslide inventory database. The statistical index based accuracy assessment in terms of Area Under the Curve (AUC) exhibits an LR model AUC of 0.776 and RF model AUC of 0.820. However, it has been observed that the RF model strongly correlates with the testing inventory dataset exhibiting that around 46% of the landslide-prone terrain is classified in the ‘high to severe’ zones with 41% inventory landslides occurring in these zones. Integrating the Random Forest (RF) based LSI thematic layer with the socio-economic vulnerability layers like the Number of Households and Population Density have demarcated around 20.78% of the region under ‘very high to severe’ socio-economic risk as convoluted by landslide susceptibility in the terrain. The present findings are expected to be useful in urban development and town planning with appropriate slope management and land-use planning.

Spatial analysis of multivariate factors influencing suicide hotspots in Urban Tamil Nadu

Clusters of suicide occur when there is a concentration of frequent incidents happening closely together in both time and space. Despite advancements in technology, there has been a persistent increase in suicide instances that has proven challenging to effectively manage. This research utilizes longitudinal and spatiotemporal data sourced from reports by the Indian Ministry of Home Affairs and the Tamil Nadu government. The objective is to comprehensively examine the spatial dimension of suicide along with its influencing factors, employing scan statistics for the years 2020 and 2021. Hypothesis testing is conducted to compare hotspot and coldspot locations consistently identified within the study period of 2012-2021. To unravel the underlying causes of suicide within the consistent hotspot zone, a multivariate analysis approach is employed. Notably, this study highlights Madurai, a major city in Tamil Nadu, as a statistically significant and enduring hotspot for suicide. The analysis identifies economic factors as the primary catalyst for the escalating suicide rates in this hotspot region. This emphasizes the urgent need for increased attention to address this issue effectively. State-level assessment of suicide instances and its concerning factors, the restrictive measure of suicide instances due to availability of data sources. This study provides a roadmap that aims to assist health and governmental entities in formulating, strategizing, and implementing feasible preventive measures specifically tailored to curbing suicide incidents in urban Tamil Nadu.

Harnessing the druggability at orthosteric and allosteric sites of PD-1 for small molecule discovery by an integrated in silico pipeline

The PD-1/PD-L1 interaction is a promising target for small molecule inhibitors in cancer immunotherapy, but targeting this interface has been challenging. While efforts have been made to identify compounds that target the orthosteric sites, no reports have explored the potential of small molecules to target the allosteric region of PD-1. Therefore, our study aims to establish a pipeline to identify small molecules that can effectively bind to either the orthosteric or allosteric pockets of PD-1. We categorized the PD-1 interface into two hot-spot zones (P-and N-zones) based on extensive analysis of its structural, dynamical, and energetic properties. These zones correspond to the orthosteric and allosteric PPI sites, respectively, targeted by monoclonal antibodies. We used a guided virtual screening workflow to identify hits from ∼7 million compounds library, which were then clustered based on structural similarity and assessed by interaction fingerprinting. The selective and diverse chemical representatives were subjected to MD simulations and binding energetics calculations to filter out false positives and identify actual binders. Binding poses metadynamics calculations confirmed the stability of the final hits in the pocket. This study emphasizes the need for an integrated pipeline that uses molecular dynamics simulations and binding energetics to identify potential binders for the dynamic PD-1/PD-L1 interface, due to the lack of small molecule co-crystals. Only a few potential binders were discovered from a large pool of molecules targeting both the allosteric and orthosteric zones. Our results suggest that the allosteric site has more potential than the orthosteric site for inhibitor design. The identified “computational hits” hold potential as starting points for in vitro evaluations followed by hit-to-lead optimization. Overall, this study represents an effort to establish a computational pipeline for exploring and enriching both the allosteric and orthosteric sites of PPI interfaces, “a tough but indispensable nut to crack”.

Municipal wastewater treatment with limited carbon sources: The significance and advantages of transformation from partial denitrification to partial nitrification coupling with anammox

Anaerobic ammonium oxidation (anammox) combined with partial nitrification (PN) is expected to achieve energy self-sufficiency in wastewater treatment, while its advantages have not been fully revealed. An anaerobic/aerobic/anoxic (AOA) system was established to treat low chemical oxygen demand (COD)-total inorganic nitrogen (TIN) (C/N) ratio (3.08) municipal wastewater. Anammox bacteria were enriched from 7 × 106 to 3 × 108 copies/gSS during partial denitrification coupling with anammox (PDA) phase. With the PN achievement, the anammox hotspot zone transformed from the anoxic zone through PDA (37%) to the aerobic zone by partial nitrification coupled with anammox (PNA) (12%). As the nitrite accumulation rate increased to 97%, the TIN removal efficiency and rate increased to 95% and 0.12 kgN/(m3·d). Although anammox performance weakened after this transformation, the carbon sources saved by PN drove more nitrogen removal. PNA was beneficial for advanced and efficient nitrogen removal in carbon-limited wastewater treatment and was less dependent on higher anammox contribution.

Coconcentration/Colocation Effects of Quasi Fabry–Pérot Cavity Structure with Anti‐Interference Feature on Light and Analytes: Accurate Raman Detection of Hg2+ under Diverse Real Environments

AbstractA quasi Fabry–Pérot (F–P) cavity structure of zinc oxide/sliver nanowires coated with zeolitic imidazolate framework‐8 (ZnO/Ag NWs/ZIF‐8) is designed to precisely guide target analytes into the hotspot zones while hindering the entry of various potential interferents during Raman detection. Owing to the triple synergistic effect of the ZIF‐8 dielectric F–P cavity, ZIF‐8 microsponge, and backward light scattering caused by the ZnO/Ag NW tip, the structure of ZnO/Ag NWs/ZIF‐8 is verified to have unique coconcentration and colocation effects on light and analytes. The molecular sieving effect of the ZIF‐8 layer and the selectivity of the premodified reporter probe on ZnO/Ag NWs endow the ZnO/Ag NWs/ZIF‐8 with an important anti‐interference feature for diverse interferents in practical Raman detection of heavy‐metal ions. The analytical enhancement factor of ZnO/Ag NWs/ZIF‐8 is as high as 3.97 × 106, with a limit of detection for Hg2+ reaching 10−10 M; and the relative standard deviation is only 6.03%. The structure directly and successfully attains accurate Raman detection of diverse real samples containing Hg2+, without an additional pre‐extraction process. This research provides a novel way of improving Raman detection reliability under real scenarios and is a new attempt to precisely manipulate light and analytes by controlling the dielectric.